🚀 Building a Graph-Based Fraud

🚀 Building a Graph-Based Fraud Detection System with Neo4j

A single transaction may look normal. Its connections can tell a different story.

I’m building a learning project using Neo4j and Cypher to explore relationships between customers, accounts, transactions, and devices—and flag patterns worth investigating.

🔍 Patterns I’m exploring:

• Multiple customers sharing the same device • Unusually large transactions • Rapid transactions within a short period • Multiple accounts sending money to one account • Circular transfers between connected accounts

The focus is explainability: showing the relationships behind each flag so the suspicious activity is easier to understand.

Through this project, I’m strengthening my graph data modelling, Cypher queries, and rule-based detection skills.

Next: FastAPI integration, transaction risk scores, fraud alerts, and an interactive monitoring dashboard.

🛠️ Currently a learning prototype, with more updates coming as I build.

What would you investigate first: shared devices or circular transfers?

#Neo4j #GraphDatabase #FraudDetection #Cypher #BackendDevelopment #LearningInPublic